1. Field of the Invention
The present invention relates to an image processing apparatus for digitizing image data to binary values or multi-levels and, more particularly, to an image processing apparatus for half-tone processing input image data.
2. Related Background Art
Hitherto, in image processing apparatuses such as facsimile apparatus, digital copying machine, and the like, an error diffusion method and an average density approximating method have been proposed as a half-tone processing system.
The former error diffusion method has been disclosed in the literature by R. Floyd & L. Steinberg, "AN ADAPTIVE ALGORITHM FOR SPATIAL GRAY SCALE", SID 75 DIGEST, pp 36 to 37. According to the error diffusion method, multi-level image data of a target pixel is binarized (converted into a highest density level or a lowest density level) and a difference between the binary level and the multi-value image data before the binarization is added with a predetermined weight and the resultant data is added to the data of the pixel near the target pixel.
The applicant of the present invention has already filed U.S. patent application Ser. Nos. 137,439, 140,029, 145,593, 192,601, 203,880, 270,809, 284,603, 289,017 and 319,057 as techniques for half-tone processing image data by the error diffusion method.
On the other hand, as disclosed in Japanese Unexamined Patent Publication (Kokai) No. 57-104369, according to the latter average density approximating method, the target pixel is binarized to black or white by using the already binarized data of pixels near the target pixel, the weighting average values with the respective pixels near the target pixel are obtained, the average of the two average values is set to a threshold value, and the image data of the target pixel is binarized on the basis of the threshold value.
Since the above error diffusion method is of the type in which the difference between the input image data and the output image data is corrected, the densities of the input image and the output image can be preserved, so that an image having excellent resolution and gradation can be provided.
However, in the error diffusion method, when the difference between the input image data and the output image data is corrected, many two-dimensional calculations must be executed, so that there is a drawback such that a hardware construction is very complicated because of a very large processing amount.
On the other hand, in the average density approximating method, since the calculations are performed by using the binary data after completion of the binarization, a hardware construction can be simplified and a high processing speed can be realized because of a very small processing amount.
However, in the average density approximating method, the target pixel is merely approximated to the average value of the region including the target pixel and is binarized. Therefore, there are drawbacks such that the number of gradations is limited, a texture of a low frequency which is peculiar to the image having a gentle density change occurs, and the picture quality deteriorates.